The potential business value of these applications is substantial—but so are the resource and
infrastructure requirements needed to operate them at speed and scale. Training ML models that
enable these use cases requires large amounts of data, tens to thousands of compute nodes, and
enhanced inter/intra node networking.
In response to these issues, a growing number of organizations are looking to the cloud. The cloud
brings together data, low-cost storage, security, and ML services along with high-performance
compute infrastructure for model training and deployment.
How Amazon Web Services (AWS) delivers ML success
More ML happens on Amazon Web Services (AWS) than anywhere else, and AWS offers the
broadest and deepest portfolio of services to accelerate business transformation. Organizations
of all sizes, from Fortune 500 to startups, are increasingly adopting AWS because AWS offers the
ideal combination of high-performance and low-cost infrastructure services and machine learning
services optimized for ML. By running their ML workloads in the cloud, customers get on-demand
access to infrastructure and ML tools that can be spun up in minutes, scale from one to thousands
of instances, and only pay for what they use.
Let's take a look at some examples of AWS customers who are driving results with ML today.
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